Use of the Micro-Doppler Effect in Classifying and Differentiating Drones from Birds

Abstract Small Remotely Piloted Aircrafts (RPAs) are gaining an increasingly prominent role in society. However, their growing use has raised concerns due to their potential misuse for illicit activities. Consequently, the detection and accurate classification of small RPAs have become crucial. Conventional radar systems, however, struggle to distinguish these aircraft from birds, as their Radar Cross-Sections (RCS) are very similar. To address this challenge, the Micro-Doppler (MD) effect has emerged as a valuable tool, enabling the differentiation of signals between birds and small RPAs. It also facilitates the classification of these aircraft. This study analyzed the Micro-Doppler (MD) effect generated by the motion of the rotating components of quadcopter RPAs and explored how this phenomenon can be utilized to extract information, such as rotor rotation speeds and propeller sizes, for an initial classification of these aircraft. Furthermore, the MD effect produced by birds was examined to identify characteristics that can be used to differentiate RPAs from birds. Lastly, the results of MD effect measurements using a Frequency-Modulated Continuous Wave (FMCW) radar during the RPA FIMI X8 SE flight were analyzed and discussed.